Skip to content

BobXWu/DecTM

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Check our latest topic modeling toolkit TopMost !

Code for Discovering Topics in Long-tailed Corpora with Causal Intervention

ACL2021 Findings

Usage

0. Prepare environment

Requirements:

python==3.6
tensorflow-gpu==1.13.1
scipy==1.5.2
scikit-learn==0.23.2

1. Prepare data

Download preprocessed datasets from Google Drive and extract files to the path ./data.

2. Run the model

python main.py --data_dir ./data/{dataset} --output_dir ./output

3. Evaluation

topic coherence: coherence score.

topic diversity:

python utils/TU.py --data_path {path of topic word file}

Citation

If you are interested in our work, please cite as

@inproceedings{wu2021discovering,
    title = "Discovering Topics in Long-tailed Corpora with Causal Intervention",
    author = "Wu, Xiaobao  and
    Li, Chunping  and
    Miao, Yishu",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.15",
    doi = "10.18653/v1/2021.findings-acl.15",
    pages = "175--185",
}

Other related works

EMNLP2020 Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder

NLPCC2020 Learning Multilingual Topics with Neural Variational Inference

About

Code for Discovering Topics in Long-tailed Corpora with Causal Intervention (ACL findings2021)

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages